Clinical trials are playing an increasingly crucial role in modern evidence based medicine, allowing for rigorous scientific evaluation of treatment strategies and validation of patient care. The results of clinical trials often form the rational basis from which physicians draw information used to adapt their therapeutic practices. Critical reading and analysis of trials involves the assessment of whether the available data provide enough credible evidence that the treatment will result in a clinically significant and relevant improvement. Evaluating the quality of a clinical trial is a process that draws upon sometimes complex methodological and statistical concepts, with which the reader should nonetheless be familiar in order to come to impartial conclusions regarding the raw data presented in the clinical trials. The goal of the current article is to review the methodological and statistical concepts required for the design and interpretation of clinical trials, so as to allow for a critical analysis of publications or presentations of clinical trials. The first section describes the major methodological principles of clinical trial design required for a rigorous evaluation of the treatment benefit, as well as the various pitfalls or biases that could lead to erroneous conclusions. The second section briefly describes the main statistical tests used in clinical trials, as well as certain situations that may increase the risk of false positive findings (type 1 error), such as multiple, subgroup, intermediate and non-inferiority analysis.